The feature-based medical image registration methods can be classified into point-based approaches purchase abana 60pills without prescription, for example generic abana 60 pills mastercard, Fitzpatrick abana 60pills amex, West, and Maurer (1998), curve-based algorithms, for example, Maintz et al. Point-based registration involves identifying the corresponding points, matching the points, and inferring the image transformation. The corresponding points are also called homologous landmarks to emphasize that they should present the same feature in the different images. These points can either be anatomical features or markers attached to the patient, which can be identified in both images modalities. Anatomical landmark based registration methods have the drawback of user interaction being required. Registration algorithms based on extrinsic landmarks which maybe invasive or non-invasive, are comparatively easy to implement, fast, and can be automated, but they may have drawbacks of invasiveness and less accurate results. As a successful example, iterative closest points (ICP) method proposed by Besl and McKay (1992), maybe the most widely used medical image registration approach in medical imaging applications, for example, Fitzpatrick, West, and Maurer (1998). When points are available, Thin-Plate Splines (TPS) which produce a smoothly interpo- lated spatial mapping, are often used to determine the transformation for 2-D medical image registration, for example, Bookstein (1989). Boundaries or surfaces are distinct features in medical image registration due to various segmentation algorithms which can successfully locate such features. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. Feature-based registration procedure Preprocessing Isolation of features for direct comparison (Segmentation) 1. Computation of the optimal transformations to register features in B onto features in A. Usually through visual check Verifying Phantom Studies… In rigid surface-based registration methods, the same anatomical structure surfaces are extracted from the images and used as input for the registration procedure. The first surface extracted from the higher-resolution images, is represented as a stack of discs, and is referred to as “head”. The second surface, referred to as “hat”, is represented as a list of unconnected 3D points. The registration is determined by iteratively transforming the hat surface with respect to the head surface, until the closest fit of the hat onto the head is found. Because the segmentation task is comparatively easy, and the computational cost is relatively low, this method remains popular. In deformable surface-based registration methods, the extracted surfaces or curves from one image is elastically deformed to fit the second image. The deformable curves are known as snakes or active contours which help to fit contours or surfaces to image data.

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